WIll humans-in-the-loop become borgs? merits and pitfalls of working with AI

Andreas Fügener, Jörn Grahl, Alok Gupta, Wolfgang Ketter

Research output: Contribution to journalArticlepeer-review

133 Scopus citations

Abstract

We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.

Original languageEnglish (US)
Pages (from-to)1527-1556
Number of pages30
JournalMIS Quarterly: Management Information Systems
Volume45
Issue number3
DOIs
StatePublished - Sep 1 2021

Bibliographical note

Publisher Copyright:
©2021. The Authors. Published by the Management Information Systems Research Center at the University of Minnesota. This is an open access article under the terms of the Creative Commons Attribution CC BY License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

  • AI–human complementarity
  • Analytical model
  • Artificial intelligence
  • Future of work
  • Machine learning
  • Unique human knowledge
  • Wisdom of crowds

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